3D virtual histopathology of cardiac tissue from Covid-19 patients based on phase-contrast X-ray tomography

  1. Marius Reichardt
  2. Patrick Moller Jensen
  3. Vedrana Andersen Dahl
  4. Anders Bjorholm Dahl
  5. Maximilian Ackermann
  6. Harshit Shah
  7. Florian Länger
  8. Christopher Werlein
  9. Mark P Kuehnel
  10. Danny Jonigk  Is a corresponding author
  11. Tim Salditt  Is a corresponding author
  1. Institut für Röntgenphysik, Georg-August-Universität Göttingen, Friedrich-Hund-Platz, Germany
  2. Technical University of Denmark, Richard Petersens Plads, Denmark
  3. Institute of Anatomy and Cell Biology, University Medical Center of the Johannes Gutenberg-University Mainz, Germany
  4. Medizinische Hochschule Hannover (MHH), Germany
  5. Deutsches Zentrum für Lungenforschung (DZL), Hannover (BREATH), Germany
9 figures, 6 tables and 1 additional file


Sample preparation and tomography setups.

(A) HE stain of a 3-m-thick paraffin section of one sample from a patient who died from Covid-19 (Cov-I, Scalebar: 100μm). In total, 26 postmortem heart tissue samples were investigated: 11 from Covid-19 patients, 4 from influenza patients, 5 from patients who died with myocarditis and six control samples. (B) From each of the samples, a biopsy punch with a diameter of 3.5mm was taken and transferred onto a holder for the tomography acquisition. After tomographic scans of all samples at the laboratory setup, Covid-19 and control specimens were investigated at the synchrotron. Furthermore,at the laboratory and parallel beam setup at the synchrotron, one punch with a diameter of 1mm was taken from one of the control and Covid-19 samples for investigations at high resolution. (C) Sketch of the laboratory micro-CT setup. Tomographic scans of all samples were recorded in cone beam geometry with an effective pixel size of pxeff=2μm using a liquid metal jet source (EXCILLUM, Sweden). (D) Sketch of the parallel beam setup of the GINIX endstation (P10 beamline, DESY, Hamburg). In this geometry, datasets of Covid-19 and control samples were acquired at an effective voxel size of 650nm3. One plane of each sample was covered by 3×3 tomographic recordings. For each sample a plane of 3×3 tomographic acquisitions was recorded. (E) Cone beam setup of the GINIX endstation. After the investigation in parallel geometry, the 1 mm biopsy punches of one control and Covid-19 sample were probed and a high resolution scan in cone beam geometry was recorded. This configuration is based on a coherent illumination by a wave guide and allows for high geometric magnification and effective voxel sizes below 200nm.

Data analysis workflow of cardiac samples.

(A) Volume rendering of a tomographic reconstruction from PB data. (B) Orthogonal slice of the masked tissue. Scale bar: 1mm (C) Shape measure distribution (Cl red, Cp green and Cs blue) of the slice shown in B. (D) Ternary plot of shape measure distribution. The peak (red) and mean (yellow) values are marked with an asterisk. (E) Overview of the training process for the neural network. (1) Random subvolumes (containing labeled voxels) are sampled from the full volume and are collected in a batch. (2) The batch is fed through the neural network, resulting in (3) a segmentation (top) and labels for one subvolume (bottom). (4) The dice loss is computed from segmented subvolumes based on labeled voxels, and the parameters of the neural network are updated. (F) Scheme of branching and the relation to degree of the vessel nodes obtained by a graph representation of the segmented microvasculature.

Overview of reconstruction volumes: Laboratory setup.

For each sample analyzed at the LJ µ-CT setup one slice of the reconstructed volume is shown. In the top row, a slice of a tomographic reconstruction of a control sample (Ctr-I) and of a sample from a patient who died from Covid-19 (Cov-I) are shown. Below, further slices from control (Ctr-II to Ctr-VI), Covid-19 (Cov-II to Cov-XI) as well as myocarditis (Myo-I to Myo-V) and influenza (Inf-I to Inf-IV) samples are shown. Scale bars: 1mm.

High-resolution tomogram of cardiac tissue recorded in cone beam geometry.

(A) Volume rendering of a tomographic reconstruction from a control sample recorded in cone beam geometry based on a wave guide illumination. After the analysis in parallel beam geometry, a biopsy with a diameter of 1mm was taken from the 3.5mm biopsy punch. This configuration revealed sub-cellular structures such as nuclei of one cardiomyocytes, myofibrils and intercalated discs. (B) Slice of the reconstructed volume perpendicular to the orientation of the cardiomyocytes. The red box marks an area which is magnified and shown on the right. One cardiomyocyte is located in the center of the magnified area. In this view, the nucleus can be identified. It contains two nucleoli, which can be identified as dark spots. The myofibrils appear as round discs. (C) Orthogonal slice which oriented along the orientation of the cardiomyocytes. A magnification of the area marked with a red box. In this view, a nucleus but also the myofibrils can be identified as dark, elongated structures in the cell. Further, an intercalated disc is located at the bottom of the area. (D) Volume rendering of a tomographic reconstruction from a Covid-19 sample. Slices orthogonal (E) and along (F) to the cardiomyocyte orientation are shown on the right. In the magnified areas, a nucleus of an endothelial cell and an intraluminar pillar -the morphological hallmark of intussusceptive angiogenesis- are visible. Scale bars: orthoslices 50μm; magnified areas 10μm.

Clustering of LJ data sets.

(A) Ternary diagram of the mean value of the shape measures for all datasets. The control samples (green) show low Cs values, while samples from Covid-19 (red), influenza and myocarditis (blue) patients show a larger variance for Cs. (B) The fitted area of the elliptical fit from the PCA analysis of the shape measure distribution is an indicator for the variance in tissue structure. For Control and influenza sample this value differs significantly from the Covid-19 tissue. (C) The eccentricity of the fit indicates if the structural distribution in shape measure space has a preferred direction along any axis. The value of the myocarditis samples is comparable low.

Segmentation of the vascular system in cardiac samples.

(A) Segmentation of the vessels of a Ctr sample. The vessels are well oriented and show a relatively constant diameter. (B) Segmentation of the vessels of a Covid-19 sample. The vessels show large deviations in diameter and the surface of the vessels is not as smooth as in the control sample. (C) Filtered minimum projection of an area of the reconstructed electron density of the Cov sample to highlight a vessel loop marked in blue. (D) Surface rendering of the segmented vessel and vessel graph in an area of the Cov sample. Scale bars 25μm. (E) Comparison of node degree n between control and Covid-19. Ratio refers to the number of graph branch points (n > 2) divided by the number of end points (n = 1). (F) Exemplary scanning electron microscopy image of a microvascular corrosion casting from a Covid-19 sample. The black arrows mark the occurrence of some tiny holes indicating intraluminar pillars with a diameter of 2μm to 5μm, indicating intussusceptive angiogenesis. Magnification x800, scale bar 20μm.

Appendix 1—figure 1
HE stain of all cardiac samples .

Scale bar: 5mm.

Appendix 1—figure 2
Reconstructions of the LJ compared to the PB setup.

Comparison of the data quality of laboratory and synchrotron measurements. (A) slice of a laboratory reconstruction at a voxelsize of 2μm. A region of interest containing a branching vessel is marked by a blue box which is shown in (B). The same area cropped from a tomographic reconstruction at the PB setup at a voxelsize of 650nm is shown in (C). The smaller voxelsize, higher contrast and SNR of the PB scans is necessary to segment the vascular system. Scale bars: (A) 1mm, (B,C).50μm

Appendix 1—figure 3
Shape measure of all Covid-19 and control samples reconstructed from PB data.

Slices of the reconstructed electron density (stitched volumes of 3 ×three tomographic reconstructions), the corresponding slice of the shape measure and the ternary plot of the shape distribution in the entire volume are shown. Corrupted datasets were excluded from the analysis and masked in white. Scale bar: 1mm.


Table 1
Sample and medical information of patients.
Sample groupN patientsSample quantityAgeSex
Control2631 ± 72 F
Covid-19111176 ± 1310 M, 1 F
Myocarditis5543 ± 174 M, 1 F
Influenza4463 ± 93 M, 1 F
Table 2
Data acquisition parameters of the laboratory and synchrotron scans.
ParameterLJ setupPB setupWG setup (Ctr/Cov)
Photon energy (keV)9.2513.810/10.8
Source-sample-dist. x01 (m)0.092900.125/0.125 0.127 0.131 0.139
Sample-detector-dist. x12 (m)0.2060.54.975
Geometric magnification M3140
Pixel size (µm)6.50.656.5
Effective pixel size (µm)20.650.159
Field-of-view h×v (mm2)4.8×3.41.6× 1.40.344×0.407/0.325× 0.325
Acquisition time (s)3× 0.60.0350.3/2.5
Number of projections150130001500
Number of flat fieldempties50100050
Number of dark field5015020
Table 3
Phase retrieval algorithms and parameters used for the different setups.
SetupLJ setupPB setup configurationWG setup configuration
Fresnel number0.471250.00950.0017
phase retrievalBACCTFnonlinear CTF
γ= 1α2= 0.5α2= 0.2
Table 4
Parameters of the cardiac tissue obtained from LJ reconstructions.

For all sample groups the mean value and standard deviation of the mean shape measures μl¯, μp¯, μs¯ area of the elliptical fit Aη¯ (%) and the eccentricity e¯ is shown.

Control0.60± 0.110.18± 0.070.22± 0.0611.98± 6.420.61± 0.13
Covid-190.44±0.120.23±0.030.32±0.1116.92± 2.910.61± 0.09
Myocarditis0.47±0.140.21± 0.020.33±0.1316.69± 5.060.51± 0.12
Influenza0.49±0.110.16±0.020.35±0.1213.44± 1.310.63± 0.07
Appendix 2—table 1
Sample and medical information.

Age and sex, clinical presentation with hospitalization and treatment. RF:respiratory failure, CRF: cardiorespiratory failure, MOF: multi-organ failure, V: ventilation, S: Smoker, D: Diabetes TypeII, H: Hypertension, I: imunsupression

Sample no.Age, sexHospitalization (days), clinical, radiological and histological characteristics
Cov-I86,M5d, RF, D, H, I
Cov-II96,M3d, RF, H
Cov-III78,M3d, CRF, V, D, S, H
Cov-IV66,M9d, RF, V, S, H
Cov-V74,M3d, RF, D, S, H
Cov-VI81,F4d, RF, S, H
Cov-VII71,M0d, V
Cov-VIII88,M2d, V, H, I
Cov-IX85,M5d, V, S, H
Cov-X58,M7d, V, H
Cov-XI54,M15d, V
Ctr-I to Ctr-III26, F-
Ctr-IV to Ctr-VI36, F-
Myo-I57,MV, H
Myo-III59,MS, H, D
Myo-IV50,MV, S, D
Inf-I74,M9d, CRF into MOF, V, S, H
Inf-II66,F17d, MOF, V, H
Inf-III56,M3d, CRF into MOF, V
Inf-IV55,M24d, RF into MOF, V, S
Appendix 2—table 2
Parameters of the cardiac tissue (laboratory data).
SampleMean (Cl,Cp, Cs)Fitted areaEccentricity
Ctr-I(0.6508, 0.1069, 0.2423 )7.31940.5607
Ctr-II( 0.5167, 0.1907, 0.2926 )11.51300.5736
Ctr-III( 0.5074, 0.2427, 0.2499)23.74430.4128
Ctr-IV( 0.7434, 0.1166, 0.1400 )5.90260.6757
Ctr-V( 0.7038, 0.1495, 0.1467 )9.57630.7896
Ctr-VI( 0.4765, 0.2835, 0.2400 )13.79730.6688
mean(0.60 ± 0.11. 0.18 ± 0.07, 0.22 ± 0.06)11.98 ± 6.420.61 ± 0.13
Cov-I( 0.5398, 0.2327, 0.2275)12.70520.6696
Cov-II( 0.4676, 0.2550, 0.2774 )17.03470.6059
Cov-III( 0.5896, 0.2526, 0.1578)11.88450.7399
Cov-IV( 0.5911, 0.1833, 0.2255 )16.30400.6765
Cov-V( 0.3371, 0.2505, 0.4124)16.34450.4081
Cov-VI( 0.5184, 0.2279, 0.2537)19.19540.6044
Cov-VII(0.3912, 0.2262, 0.3826)19.82060.6530
Cov-VIII( 0.5227, 0.1776, 0.2997)15.07910.6033
Cov-IV(0.3253, 0.2851, 0.3897 )20.57680.5329
Cov-X(0.3283, 0.2446, 0.4271 )16.99890.6266
Cov-XI( 0.2484, 0.2314, 0.5202 )20.18150.5407
mean(0.44±0.12,0.23±0.03,0.32±0.11 )16.92 ± 2.910.61 ± 0.09
Myo-I(0.5777, 0.2018, 0.2206 )9.55280.4656
Myo-II(0.3887, 0.1943, 0.4170 )13.78530.4899
Myo-III( 0.5984, 0.2081, 0.1935 )22.47680.6202
Myo-IV( 0.4974, 0.1908, 0.3117 )18.3306?0.6149
Myo-V(0.2664, 0.2402, 0.4933 )19.32120.3689
mean(0.27 ± 0.14, 0.24 ± 0.02, 0.49 ± 0.13)16.69 ± 5.060.51 ± 0.12
Inf-I(0.3561, 0.1714, 0.4724 )14.93930.6808
Inf-II( 0.4423, 0.1376, 0.4201 )11.7445?0.5991
Inf-III( 0.6150, 0.1361, 0.2489 )13.59880.7198
Inf-IV( 0.5404, 0.1849, 0.2747 )13.48850.5561
mean(0.49 ± 0.11, 0.16 ± 0.02, 0.35 ± 0.11)13.44 ± 1.310.63 ± 0.07

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  1. Marius Reichardt
  2. Patrick Moller Jensen
  3. Vedrana Andersen Dahl
  4. Anders Bjorholm Dahl
  5. Maximilian Ackermann
  6. Harshit Shah
  7. Florian Länger
  8. Christopher Werlein
  9. Mark P Kuehnel
  10. Danny Jonigk
  11. Tim Salditt
3D virtual histopathology of cardiac tissue from Covid-19 patients based on phase-contrast X-ray tomography
eLife 10:e71359.